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GLOSSARY FOR ISYE 6501 INTRODUCTION TO ANALYTICS MODELING NEWEST ACTUAL QUESTIONS AND VERIFIED SOLUTIONS|ALREADY GRADED A+ 100%TUTOR VERIFIED

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GLOSSARY FOR ISYE 6501 INTRODUCTION TO ANALYTICS MODELING NEWEST ACTUAL QUESTIONS AND VERIFIED SOLUTIONS|ALREADY GRADED A+ 100%TUTOR VERIFIED

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  • December 22, 2024
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  • 2024/2025
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  • glossary for isye 6501 introduction to analytics

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GLOSSARY FOR ISYE 6501 INTRODUCTION TO
ANALYTICS MODELING NEWEST ACTUAL
QUESTIONS AND VERIFIED SOLUTIONS|ALREADY
GRADED A+ 100%TUTOR VERIFIED
Algorithm - (answers)Step-by-step procedure designed to carry out a task.


Change detection - (answers)Identifying when a significant change has taken
place in a process.


Classification - (answers)The separation of data into two or more categories, or
(a point's classification) the category a data point is put into.


Classifier - (answers)A boundary that separates the data into two or more
categories. Also (more generally) an algorithm that performs classification.


Cluster - (answers)A group of points identified as near/similar to each other.


Cluster center - (answers)In some clustering algorithms (like 𝑘𝑘-means
clustering), the central point (often the centroid) of a cluster of data points.


Clustering - (answers)Separation of data points into groups ("clusters") based
on nearness/similarity to each other. A common form of unsupervised learning.


CUSUM - (answers)Change detection method that compares observed
distribution mean with a threshold level of change. Short for 'cumulative sum'.


Deep learning - (answers)Neural network-type model with many hidden layers.

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Dimension - (answers)A feature of the data points (for example, height or
credit score).


EM algorithm - (answers)Expectation-maximization algorithm.


Expectation-maximization algorithm (EM algorithm) - (answers)General
description of an algorithm with two steps (often iterated), one that finds the
function for the expected likelihood of getting the response given current
parameters, and one that finds new parameter values to maximize that
probability.


Heuristic - (answers)Algorithm that is not guaranteed to find the absolute best
(optimal) solution.


𝑘𝑘-means algorithm - (answers)Clustering algorithm that defines 𝑘𝑘 clusters of
data points, each corresponding to one of 𝑘𝑘 cluster centers selected by the
algorithm.


𝑘𝑘-Nearest-Neighbor (KNN) - (answers)Classification algorithm that defines a
data point's category as a function of the nearest 𝑘𝑘 data points to it.


Kernel - (answers)A type of function that computes the similarity between two
inputs; thanks to what's (really!) sometimes known as the 'kernel trick',
nonlinear classifiers can be found almost as easily as linear ones.


Learning - (answers)Finding/discovering patterns (or rules) in data, often that
can be applied to new data.


Machine - (answers)Apparatus that can do something; in 'machine learning', it
often refers to both an algorithm and the computer it's run on.

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Margin - (answers)For a single point, the distance between the point and the
classification boundary; for a set of points, the minimum distance between a
point in the set and the classification boundary.


Machine learning - (answers)Use of computer algorithms to learn and discover
patterns or structure in data, without being programmed specifically for them.


Misclassified - (answers)Put into the wrong category by a classifier.


Neural network - (answers)A machine learning model that itself is modeled
after the workings of neurons in the brain.


Supervised learning - (answers)Machine learning where the 'correct' answer is
known for each data point in the training set.


Support vector - (answers)In SVM models, the closest point to the classifier,
among those in a category.


Support vector machine (SVM) - (answers)Classification algorithm that uses a
boundary to separate the data into two or more categories ('classes').


SVM - (answers)Support vector machine.


Unsupervised learning - (answers)Machine learning where the 'correct' answer
is not known for the data points in the training set.


Voronoi diagram - (answers)Graphical representation of splitting a plane with
two or more special points into regions with one special point each, where

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each region's points are closer to the region's special point than to any other
special point.


Accuracy - (answers)Fraction of data points correctly classified by a model;
equal to 𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇 / 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.


Confusion matrix - (answers)Visualization of classification model performance.


Diagnostic odds ratio - (answers)Ratio of the odds that a data point in a certain
category is correctly classified by a model, to the odds that a data point not in
that category is incorrectly classified by the model.


Fall out - (answers)Fraction of data points not in a certain category that are
incorrectly classified by a model; equal to 𝐹𝐹𝐹𝐹 / 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.


False negative (FN) - (answers)Data point that a model incorrectly classifies as
not being in a certain category.


False negative rate - (answers)Fraction of data points in a certain category that
are incorrectly classified by a model; equal to 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.


False positive (FP) - (answers)Data point that a model incorrectly classifies as
being in a certain category.


False positive rate - (answers)Fraction of data points not in a certain category
that are incorrectly classified by a model; equal to 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.


False omission rate - (answers)Fraction of data points the model classifies as
not in a certain category, that are really in the category.

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